Machine vision based automatic crop or weed detection in commercial fields is still an open problem, because of wide variety of plant species and the unstructured lighting condition. The task becomes even more challenging when weeds are prominent and overlap with crops. This paper presents an image processing method for fast discrimination of crops and weeds in fields with high weed infestation levels. Mahalanobis distance was used to classify crop and weed pixels in field images based on their differences in hue and saturation. A Naïve Bayes classifier was built to compare with the Mahalanobis distance based classifier. 80 images (100 by 100 pixels) of celery cabbage, broccoli and weeds were used to train and evaluate the method. Evaluation result showed that this method correctly discriminated 68.0% of crop pixels and 83.2% weed pixels in celery cabbage and weed images, and 97.0% of crop pixels and 99.7% of weed pixels in broccoli and weed images. The average time requirement for processing each 100-by-100-pixel image was 9.7 ms. Compared with the Naïve Bayes classifier, the Mahalanobis distance based classifier was more suitable to address the problem of this study. In addition, this method was built into a crop detection method designed in our previous work. A series of 15 field images of broccoli with high weed pressure were used to test the combined method. The results indicated that the combined method correctly detected 93.6% of the cops, a significant improvement over the previous method.